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Automated Video Dehazing & Atmospheric Haze Simulation System
🚀 Project Overview
This advanced Computer Vision project is designed to address visibility challenges in adverse weather conditions. The system features a dual-module architecture: it can synthetically inject realistic atmospheric fog/haze into crystal-clear video streams for dataset generation, and conversely, restore heavily degraded, foggy videos into crisp, high-visibility outputs in real-time.
🛠️ Core Functionality & Modules
Module 1: Atmospheric Haze Simulation
Purpose: Generates synthetic datasets to train and benchmark object detection models (like YOLO) for bad weather conditions.
How it works: Implements mathematical scattering models to calculate depth maps and overlay a realistic layer of dense fog or smoke over clean video frames.
Module 2: Real-Time Video Dehazing
Purpose: Restores clarity and vivid color to video streams captured in low-visibility environments.
How it works: Leverages physics-based Computer Vision algorithms (such as Dark Channel Prior - DCP) or Deep Learning frameworks to estimate atmospheric light, eliminate transmission noise, and reconstruct the scene's original contrast.
🎯 Use Cases & Applications
Autonomous Vehicles: Enhances the sight and reliability of self-driving car sensors in dense fog.
Smart Surveillance (CCTV): Improves security monitoring and facial recognition accuracy under harsh outdoor weather.
Drone Navigation: Aids aerial drones in safely navigating through smoke, dust storms, or low-lying clouds.
💻 Tech Stack Used
Language: Python
Libraries: OpenCV, NumPy, Matplotlib, PyTorch / TensorFlow (if deep learning was applied)
Concepts: Image Processing, Atmospheric Scattering Models, Feature Restoration, Video Pipeline Optimization
